Guest Editorial Special Issue on Preference-Based Multiobjective Evolutionary Algorithms

Multiobjective optimization deals with finding and evaluating a number of trade-off optimal solutions. Evolutionary multiobjective optimization (EMO), started in early nineties, is now a fast-growing field of research and application in evolutionary computation. Numerous different algorithms have been developed to address computationally complex problems. Many of these algorithms attempt to find an approximation of the efficient frontier. In particular, bi-criteria problems have been exploited extensively. Typically, the size of the efficient frontier increases substantially with the number of objectives and it becomes harder to generate all efficient solutions. This then makes a strong case for using preference-based methodologies within an EMO algorithm to handle a large number of objectives, often encountered in practical problems.